Bayesian Optimization methods including Sequential Model-based Algorithm Configuration (SMAC) has been very popular in AutoML [1]}. These methods are beneficial for optimizing expensive to evaluate functions such as the performance of supervised learning algorithms. The idea of Bayesian Optimization is extended to RL hyper-parameters optimization in [2]}. In this work, RLOpt framework uses Gaussian process regression as surrogate function and integrates Bayesian optimization with RL. The process of hyper-parameters optimization is considered as a supervised learning problem where the parameters are the input and the performance is the target. At each step, the selected parameters are given to the agent and it learns a policy and returns the performance. Based on the history of (hyper-parameter, performance) tuples, Gaussian Process is used to select the next hyper-parameters settings.
